Advanced search
Publication Cover
European Journal of Remote Sensing Volume 56, 2023 - Issue 1
Submit an article Journal homepage
948
Views
2
CrossRef citations to date
0
Altmetric
Future of Urban Remote Sensing and New Sensors

Research on the application of big data visualization technology in urban road congestion

Haitao Guoa Department of Mechanical and Electrical Engineering, Guangdong Construction Polytechnic, Guangzhou, Guangdong, China;b School of Civil Engineering and Transportation, South China University of Science and Engineering, Guangzhou, Guangdong, ChinaCorrespondence[email protected] [email protected]
&
Lunhui Xub School of Civil Engineering and Transportation, South China University of Science and Engineering, Guangzhou, Guangdong, China
Article: 2147448 | Received 12 Mar 2022, Accepted 10 Nov 2022, Published online: 08 Dec 2022

References

  • Brennan, T. M., Gurriell, R. A., Bechtel, A. J., & Venigalla, M. M. (2019). Visualizing and evaluating interdependent regional traffic congestion and system resiliency, a case study using big data from probe vehicles. Journal of Big Data Analytics in Transportation, 1(1), 25–12. https://doi.org/10.1007/s42421-019-00002-y
  • Cai, L., Zhou, Y., Liang, Y., & He, J. (2018). Research and application of gps trajectory data visualization. Annals of Data Science, 5(1), 43–57. https://doi.org/10.1007/s40745-017-0132-1
  • Carter, E., Adam, P., Tsakis, D., Shaw, S., Watson, R., & Ryan, P. (2020). Enhancing pedestrian mobility in smart cities using big data. Journal of Management Analytics, 7(2), 173–188. https://doi.org/10.1080/23270012.2020.1741039
  • Celes, C., Boukerche, A., & Loureiro, A. A. (2019). Crowd management: A new challenge for urban big data analytics. IEEE Communications Magazine, 57(4), 20–25. https://doi.org/10.1109/MCOM.2019.1800640
  • Chen, X., Wang, H. H., & Tian, B. (2019). Visualization model of big data based on self-organizing feature map neural network and graphic theory for smart cities. Cluster Computing, 22(6), 13293–13305. https://doi.org/10.1007/s10586-018-1848-1
  • Grayson, J. (2020). Big data analytics and sustainable urbanism in internet of things-enabled smart governance. Geopolitics, History, and International Relations, 12(2), 23–29. https://dx.doi.org/10.22381/GHIR12220203
  • Kong, X., Li, M., Li, J., Tian, K., Hu, X., & Xia, F. (2019). CoPFun: An urban co-occurrence pattern mining scheme based on regional function discovery. World Wide Web, 22(3), 1029–1054. https://doi.org/10.1007/s11280-018-0578-x
  • Kuftinova, N. G., Ostroukh, A. V., Karelina, M. Y., Matyukhina, E. N., & Akhmetzhanova, E. U. (2021). Hybrid smart systems for big data analysis. Russian Engineering Research, 41(6), 536–538. https://doi.org/10.3103/S1068798X21060137
  • Kumar, D., Wu, H., Rajasegarar, S., Leckie, C., Krishnaswamy, S., & Palaniswami, M. (2018). Fast and scalable big data trajectory clustering for understanding urban mobility. IEEE Transactions on Intelligent Transportation Systems, 19(11), 3709–3722. https://doi.org/10.1109/TITS.2018.2854775
  • Liu, F., Andrienko, G., Andrienko, N., Chen, S., Janssens, D., Wets, G., & Theodoridis, Y. (2020). Citywide traffic analysis based on the combination of visual and analytic approaches. Journal of Geovisualization and Spatial Analysis, 4(2), 1–17. https://doi.org/10.1007/s41651-020-00057-4
  • Shan, Z., Pan, Z., Li, F., Xu, H., & Li, J. (2018). Visual analytics of traffic congestion propagation path with large scale camera data. Chinese Journal of Electronics, 27(5), 934–941. https://doi.org/10.1049/cje.2018.04.011
  • Stipancic, J., Miranda-Moreno, L., Labbe, A., & Saunier, N. (2019). Measuring and visualizing space–time congestion patterns in an urban road network using large-scale smartphone-collected GPS data. Transportation Letters, 11(7), 391–401. https://doi.org/10.1080/19427867.2017.1374022
  • Welch, T. F., & Widita, A. (2019). Big data in public transportation: A review of sources and methods. Transport Reviews, 39(6), 795–818. https://doi.org/10.1080/01441647.2019.1616849
  • Yang, C., Clarke, K., Shekhar, S., & Tao, C. V. (2020). Big spatiotemporal data analytics: A research and innovation frontier. International Journal of Geographical Information Science, 34(6), 1075–1088. https://doi.org/10.1080/13658816.2019.1698743
  • Zhang, J., Chen, Z., Xu, Z., Du, M., Yang, W., & Guo, L. (2018). A distributed collaborative urban traffic big data system based on cloud computing. IEEE Intelligent Transportation Systems Magazine, 11(4), 37–47. https://doi.org/10.1109/MITS.2018.2876560
  • Zheng, L., Xia, D., Chen, L., & Sun, D. (2019). Understanding citywide resident mobility using big data of electronic registration identification of vehicles. IEEE Transactions on Intelligent Transportation Systems, 21(10), 4363–4377. https://doi.org/10.1109/TITS.2019.2940724
  • Zhou, Y., Lau, B. P. L., Yuen, C., Tunçer, B., & Wilhelm, E. (2018). Understanding urban human mobility through crowdsensed data. IEEE Communications Magazine, 56(11), 52–59. https://doi.org/10.1109/MCOM.2018.1700569
  • Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018). Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383–398. https://doi.org/10.1109/TITS.2018.2815678
  • Zin, T. A., Lwin, K. K., Sekimoto, Y., & Sekimoto, Y. (2018). Estimation of originating-destination trips in Yangonby using big data source. Journal of Disaster Research, 13(1), 6–13. https://doi.org/10.20965/jdr.2018.p0006

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.